{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Customizing Matplotlib with style sheets and rcParams\n",
    "=====================================================\n",
    "\n",
    "Tips for customizing the properties and default styles of Matplotlib.\n",
    "\n",
    "Using style sheets\n",
    "------------------\n",
    "\n",
    "The ``style`` package adds support for easy-to-switch plotting \"styles\" with\n",
    "the same parameters as a\n",
    "`matplotlib rc <customizing-with-matplotlibrc-files>` file (which is read\n",
    "at startup to configure matplotlib).\n",
    "\n",
    "There are a number of pre-defined styles `provided by Matplotlib`_. For\n",
    "example, there's a pre-defined style called \"ggplot\", which emulates the\n",
    "aesthetics of ggplot_ (a popular plotting package for R_). To use this style,\n",
    "just add:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "data = np.random.randn(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To list all available styles, use:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining your own style\n",
    "-----------------------\n",
    "\n",
    "You can create custom styles and use them by calling ``style.use`` with the\n",
    "path or URL to the style sheet. Additionally, if you add your\n",
    "``<style-name>.mplstyle`` file to ``mpl_configdir/stylelib``, you can reuse\n",
    "your custom style sheet with a call to ``style.use(<style-name>)``. By default\n",
    "``mpl_configdir`` should be ``~/.config/matplotlib``, but you can check where\n",
    "yours is with ``matplotlib.get_configdir()``; you may need to create this\n",
    "directory. You also can change the directory where matplotlib looks for\n",
    "the stylelib/ folder by setting the MPLCONFIGDIR environment variable,\n",
    "see `locating-matplotlib-config-dir`.\n",
    "\n",
    "Note that a custom style sheet in ``mpl_configdir/stylelib`` will\n",
    "override a style sheet defined by matplotlib if the styles have the same name.\n",
    "\n",
    "For example, you might want to create\n",
    "``mpl_configdir/stylelib/presentation.mplstyle`` with the following::\n",
    "\n",
    "   axes.titlesize : 24\n",
    "   axes.labelsize : 20\n",
    "   lines.linewidth : 3\n",
    "   lines.markersize : 10\n",
    "   xtick.labelsize : 16\n",
    "   ytick.labelsize : 16\n",
    "\n",
    "Then, when you want to adapt a plot designed for a paper to one that looks\n",
    "good in a presentation, you can just add::\n",
    "\n",
    "   >>> import matplotlib.pyplot as plt\n",
    "   >>> plt.style.use('presentation')\n",
    "\n",
    "\n",
    "Composing styles\n",
    "----------------\n",
    "\n",
    "Style sheets are designed to be composed together. So you can have a style\n",
    "sheet that customizes colors and a separate style sheet that alters element\n",
    "sizes for presentations. These styles can easily be combined by passing\n",
    "a list of styles::\n",
    "\n",
    "   >>> import matplotlib.pyplot as plt\n",
    "   >>> plt.style.use(['dark_background', 'presentation'])\n",
    "\n",
    "Note that styles further to the right will overwrite values that are already\n",
    "defined by styles on the left.\n",
    "\n",
    "\n",
    "Temporary styling\n",
    "-----------------\n",
    "\n",
    "If you only want to use a style for a specific block of code but don't want\n",
    "to change the global styling, the style package provides a context manager\n",
    "for limiting your changes to a specific scope. To isolate your styling\n",
    "changes, you can write something like the following:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('dark_background'):\n",
    "    plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "matplotlib rcParams\n",
    "===================\n",
    "\n",
    "\n",
    "Dynamic rc settings\n",
    "-------------------\n",
    "\n",
    "You can also dynamically change the default rc settings in a python script or\n",
    "interactively from the python shell. All of the rc settings are stored in a\n",
    "dictionary-like variable called :data:`matplotlib.rcParams`, which is global to\n",
    "the matplotlib package. rcParams can be modified directly, for example:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x83124e0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['lines.linewidth'] = 2\n",
    "mpl.rcParams['lines.color'] = 'r'\n",
    "plt.plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib also provides a couple of convenience functions for modifying rc\n",
    "settings. The :func:`matplotlib.rc` command can be used to modify multiple\n",
    "settings in a single group at once, using keyword arguments:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8370cc0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rc('lines', linewidth=4, color='g')\n",
    "plt.plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The :func:`matplotlib.rcdefaults` command will restore the standard matplotlib\n",
    "default settings.\n",
    "\n",
    "There is some degree of validation when setting the values of rcParams, see\n",
    ":mod:`matplotlib.rcsetup` for details.\n",
    "\n",
    "\n",
    "The :file:`matplotlibrc` file\n",
    "-----------------------------\n",
    "\n",
    "matplotlib uses :file:`matplotlibrc` configuration files to customize all kinds\n",
    "of properties, which we call `rc settings` or `rc parameters`. You can control\n",
    "the defaults of almost every property in matplotlib: figure size and dpi, line\n",
    "width, color and style, axes, axis and grid properties, text and font\n",
    "properties and so on. matplotlib looks for :file:`matplotlibrc` in four\n",
    "locations, in the following order:\n",
    "\n",
    "1. :file:`matplotlibrc` in the current working directory, usually used for\n",
    "   specific customizations that you do not want to apply elsewhere.\n",
    "\n",
    "2. :file:`$MATPLOTLIBRC` if it is a file, else :file:`$MATPLOTLIBRC/matplotlibrc`.\n",
    "\n",
    "3. It next looks in a user-specific place, depending on your platform:\n",
    "\n",
    "   - On Linux and FreeBSD, it looks in :file:`.config/matplotlib/matplotlibrc`\n",
    "     (or `$XDG_CONFIG_HOME/matplotlib/matplotlibrc`) if you've customized\n",
    "     your environment.\n",
    "\n",
    "   - On other platforms, it looks in :file:`.matplotlib/matplotlibrc`.\n",
    "\n",
    "   See `locating-matplotlib-config-dir`.\n",
    "\n",
    "4. :file:`{INSTALL}/matplotlib/mpl-data/matplotlibrc`, where\n",
    "   :file:`{INSTALL}` is something like\n",
    "   :file:`/usr/lib/python3.7/site-packages` on Linux, and maybe\n",
    "   :file:`C:\\\\Python37\\\\Lib\\\\site-packages` on Windows. Every time you\n",
    "   install matplotlib, this file will be overwritten, so if you want\n",
    "   your customizations to be saved, please move this file to your\n",
    "   user-specific matplotlib directory.\n",
    "\n",
    "Once a :file:`matplotlibrc` file has been found, it will *not* search any of\n",
    "the other paths.\n",
    "\n",
    "To display where the currently active :file:`matplotlibrc` file was\n",
    "loaded from, one can do the following::\n",
    "\n",
    "  >>> import matplotlib\n",
    "  >>> matplotlib.matplotlib_fname()\n",
    "  '/home/foo/.config/matplotlib/matplotlibrc'\n",
    "\n",
    "See below for a sample `matplotlibrc file<matplotlibrc-sample>`.\n",
    "\n",
    "\n",
    "A sample matplotlibrc file\n",
    "~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "\n",
    ".. literalinclude:: ../../../matplotlibrc.template\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
